speech-text-analytic-app.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Remove below comment to clone from my github repo: chemicopy\"\"\"\n",
    "\n",
    "# ! git clone https://github.com/chemicoPy/Speech-Text-Analytic-app.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz (from -r requirements.txt (line 11))\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz (13.7MB)\n",
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      "Requirement already satisfied: wcwidth in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=5.0.0->ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.1.7)\n",
      "Requirement already satisfied: prometheus-client in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.7.1)\n",
      "Requirement already satisfied: nbconvert in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (5.6.0)\n",
      "Requirement already satisfied: Send2Trash in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (1.5.0)\n",
      "Requirement already satisfied: terminado>=0.8.1 in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.8.2)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (1.4.2)\n",
      "Requirement already satisfied: bleach in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (3.1.0)\n",
      "Requirement already satisfied: testpath in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.4.2)\n",
      "Requirement already satisfied: defusedxml in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.6.0)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.8.4)\n",
      "Requirement already satisfied: webencodings in c:\\users\\ogunjobi victor\\anaconda3\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit==0.82.0->-r requirements.txt (line 1)) (0.5.1)\n",
      "Building wheels for collected packages: en-core-web-sm\n",
      "  Building wheel for en-core-web-sm (setup.py): started\n",
      "  Building wheel for en-core-web-sm (setup.py): finished with status 'done'\n",
      "  Created wheel for en-core-web-sm: filename=en_core_web_sm-3.0.0-cp37-none-any.whl size=13704315 sha256=5a4adfc8941d7024d11d8733de5ef29faa482107683c6a6fe2dfd3daf07b6c2f\n",
      "  Stored in directory: C:\\Users\\OGUNJOBI Victor\\AppData\\Local\\pip\\Cache\\wheels\\91\\2b\\a1\\d83336e8dfaacbbcdfc805b2c7195dd3ea10d507396fe31cac\n",
      "Successfully built en-core-web-sm\n",
      "Installing collected packages: en-core-web-sm\n",
      "Successfully installed en-core-web-sm-3.0.0\n"
     ]
    }
   ],
   "source": [
    "#!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import streamlit as st\n",
    "import time\n",
    "import glob\n",
    "import os\n",
    "from os import path\n",
    "\n",
    "# libraries for audio transcription\n",
    "from gtts import gTTS\n",
    "from googletrans import Translator\n",
    "import speech_recognition as sr\n",
    "\n",
    "# For visualizing and data manipulations\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import wave\n",
    "\n",
    "# for NLP tasks\n",
    "import spacy\n",
    "import spacy_streamlit as ss\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "# disable warnings\n",
    "st.set_option('deprecation.showPyplotGlobalUse', False)\n",
    "st.set_option('deprecation.showfileUploaderEncoding', False)\n",
    "\n",
    "img = Image.open('icon.jpeg')\n",
    "st.title('Text to Speech/AudioSpeech to Text Analytic Web App')\n",
    "st.image(img, width=650)\n",
    "st.subheader(\"Navigate to side bar to see more options\")\n",
    "\n",
    "# re-configuring page layout to restrict users from overwriting the app configuraion\n",
    "\n",
    "hide_streamlit_style = '''\n",
    "            <style>\n",
    "            #MainMenu {visibility: hidden;}\n",
    "            footer {visibility: hidden;}\n",
    "            </style>\n",
    "'''\n",
    "st.markdown(hide_streamlit_style, unsafe_allow_html=True)\n",
    "\n",
    "st.sidebar.title(\"Menu\")\n",
    "analyze = st.sidebar.selectbox(\n",
    "        '', [\"Home\", \"Text2Speech_Analytics\", \"Audio2Text_Analytics\"], index=0)\n",
    "\n",
    "\n",
    "def main():\n",
    "\n",
    "     #----- Text to Audio------------------------------------------------------\n",
    "    if analyze == \"Text2Speech_Analytics\":\n",
    "\n",
    "        # converting a text to audio would require a directory to save this file\n",
    "        # hence, play this audio\n",
    "\n",
    "        try:\n",
    "            os.mkdir(\"temp\")\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "        translator = Translator()\n",
    "        text = st.text_input(\"Enter your text\")\n",
    "        in_lang = st.selectbox(\n",
    "            \"Select your input Language\",\n",
    "            (\"English\", \"Chinese\", \"Hindi\", \"korea\", \"Japanese\", \"Spanish\"),\n",
    "        )\n",
    "        if in_lang == \"English\":\n",
    "            input_language = \"en\"\n",
    "        elif in_lang == \"Chinese\":\n",
    "            input_language = \"zh-cn\"\n",
    "        elif in_lang == \"korea\":\n",
    "            input_language = \"ko\"\n",
    "        elif in_lang == \"Hindi\":\n",
    "            input_language = \"hi\"\n",
    "        elif in_lang == \"Japanese\":\n",
    "            input_language = \"ja\"\n",
    "        # elif in_lang == \"Hausa\":\n",
    "         #   input_language = \"ha\"\n",
    "        elif in_lang == \"Spanish\":\n",
    "            input_language = \"es\"\n",
    "       # elif in_lang == \"Yoruba\":\n",
    "        #    input_language = \"yo\"\n",
    "\n",
    "        # generate output language options\n",
    "\n",
    "        out_lang = st.selectbox(\n",
    "            \"Select your output language\",\n",
    "            (\"English\", \"Chinese\", \"Hindi\", \"korea\", \"Japanese\", \"Spanish\"),\n",
    "        )\n",
    "\n",
    "        if out_lang == \"English\":\n",
    "            output_language = \"en\"\n",
    "        elif out_lang == \"Chinese\":\n",
    "            output_language = \"zh-cn\"\n",
    "        elif out_lang == \"korea\":\n",
    "            output_language = \"ko\"\n",
    "        elif out_lang == \"Hindi\":\n",
    "            output_language = \"hi\"\n",
    "     #   elif out_lang == \"Hausa\":    # It turned out google API does not support Nigerian Languages just yet.\n",
    "      #      output_language = \"ha\"\n",
    "        elif out_lang == \"Japanese\":\n",
    "            output_language = \"ja\"\n",
    "        elif out_lang == \"Spanish\":\n",
    "            output_language = \"es\"\n",
    "    #    elif out_lang == \"Yoruba\":\n",
    "     #       output_language = \"yo\"\n",
    "\n",
    "        # select english accent options for the audio output\n",
    "        english_accent = st.selectbox(\n",
    "            \"Select your english accent\",\n",
    "            (\n",
    "                \"Default\",\n",
    "                \"India\",\n",
    "                \"United Kingdom\",\n",
    "                \"United States\",\n",
    "                \"Nigeria\",\n",
    "                \"South Africa\",\n",
    "            ),\n",
    "        )\n",
    "\n",
    "        if english_accent == \"Default\":\n",
    "            tld = \"com\"\n",
    "        elif english_accent == \"India\":\n",
    "            tld = \"co.in\"\n",
    "        elif english_accent == \"United Kingdom\":\n",
    "            tld = \"co.uk\"\n",
    "        elif english_accent == \"Nigeria\":\n",
    "            tld = \"ng\"\n",
    "        elif english_accent == \"South Africa\":\n",
    "            tld = \"co.za\"\n",
    "        elif english_accent == \"United States\":\n",
    "            tld = \"com\"\n",
    "\n",
    "        def text_to_speech(input_language, output_language, text, tld):\n",
    "            translation = translator.translate(text, src=input_language, dest=output_language)\n",
    "            trans_text = translation.text\n",
    "            tts = gTTS(trans_text, lang=output_language, tld=tld, slow=False)\n",
    "            try:\n",
    "                filename = text[0:30]\n",
    "            except:\n",
    "                filename = \"audio\"\n",
    "            tts.save(f\"temp/{filename}.mp3\")\n",
    "            return filename, trans_text\n",
    "\n",
    "        display_output_text = st.checkbox(\"Display output text\")\n",
    "\n",
    "        if st.button(\"convert\"):\n",
    "            result, output_text = text_to_speech(input_language, output_language, text, tld)\n",
    "            audio_file = open(f\"temp/{result}.mp3\", \"rb\")\n",
    "            audio_bytes = audio_file.read()\n",
    "            st.markdown(f\"## Your audio:\")\n",
    "            st.audio(audio_bytes, format=\"audio/mp3\", start_time=0)\n",
    "\n",
    "            if display_output_text:\n",
    "                st.markdown(f\"## Output text:\")\n",
    "                st.write(f\" {output_text}\")\n",
    "\n",
    "\n",
    "# ------- audio to text ----------------------------------------------------\n",
    "\n",
    "    if analyze == \"Audio2Text_Analytics\":\n",
    "\n",
    "        try:\n",
    "            os.mkdir(\"temp2\")   # create directory to save our audio file to work on\n",
    "        except:\n",
    "            pass\n",
    "        st.markdown('## Upload a wav Audio File')\n",
    "        audio_file = st.file_uploader(\"Choose an audio file to upload\", type=[\"wav\"])\n",
    "        if audio_file is not None:\n",
    "            st.audio(audio_file)  # enabling users to play their audio file\n",
    "            with open(os.path.join(\"temp2\", audio_file.name), \"wb\") as f:  # saving file to a directory\n",
    "                f.write(audio_file.getbuffer())\n",
    "\n",
    "            st.success(\"File Saved : {} in temp2\".format(audio_file.name))\n",
    "\n",
    "            r = sr.Recognizer()\n",
    "            AUDIO_DIR = path.join(path.dirname(path.realpath(__file__)), \"temp2\")  # obtain the directory to saved file\n",
    "            AUDIO_PATH = path.join(AUDIO_DIR, f'{audio_file.name}')     # merge path to file to directory\n",
    "            with sr.AudioFile(AUDIO_PATH) as source:\n",
    "               audio_text = r.record(source)    # listening to audio to match language\n",
    "\n",
    "            # recognize_method will throw up request error if the google API is unreachable, we capture this error using try and except handling\n",
    "               try:\n",
    "                # using google speech recognizer\n",
    "                   text_file = r.recognize_google(audio_text)\n",
    "\n",
    "                   if st.checkbox(\"Look up your transcribed audio to text here...\"):\n",
    "                      st.write(text_file)\n",
    "\n",
    "               except:\n",
    "                    st.write('Sorry...Try Again!')\n",
    "\n",
    "            st.markdown('## Explore Different Categories of your Audio Speech')\n",
    "            fe = st.radio(label=\"Feature Extraction\", options=(' ', 'NER of SpeechText', 'Display AudioSpeech Signal', 'Analyze Speech Sentiment'))\n",
    "            if fe == \"Display AudioSpeech Signal\":\n",
    "                wav = wave.open(AUDIO_PATH, 'rb')  # open wave file using the wave library\n",
    "                raw = wav.readframes(-1)        # reading the entire wave audio frame which returns the frame as a byte object\n",
    "                raw = np.frombuffer(raw, \"int16\")  # we use numpy to convert audio bytes into an array\n",
    "                sample_rate = wav.getframerate()\n",
    "\n",
    "                Time = np.linspace(0, len(raw)/sample_rate, num=len(raw))\n",
    "                fig, ax = plt.subplots()\n",
    "                plt.plot(Time, raw, color='blue')\n",
    "                ax.set_xlabel('Time (seconds)')\n",
    "                ax.set_ylabel('Amplitude')\n",
    "                ax.set_title('Input Audio Signal')\n",
    "                plt.tight_layout()\n",
    "                st.pyplot(fig)\n",
    "\n",
    "            if fe == \"NER of SpeechText\":\n",
    "                text_file = text_file\n",
    "                nlp = spacy.load('en_core_web_sm')\n",
    "                docx = nlp(text_file)\n",
    "                if st.button(\"SpeechText Attributes\"):\n",
    "                   ss.visualize_tokens(docx)\n",
    "                   ss.visualize_ner(docx, labels=nlp.get_pipe('ner').labels)\n",
    "\n",
    "            if fe == \"Analyze Speech Sentiment\":\n",
    "                sia = SentimentIntensityAnalyzer()\n",
    "                t = sia.polarity_scores(text_file)\n",
    "                if st.button(\"Predict\"):\n",
    "                    st.write(\"Neutral, Positive and Negative value of your speech is:\")\n",
    "                    st.write(t['neu'], t['pos'], t['neg'])\n",
    "                    if t['neu'] > t['pos'] and t['neu'] > t['neg'] and t['neu'] > 0.85:\n",
    "                        st.markdown(\"Speech Text is classified as **Neutral**. :confused:\")\n",
    "                        st.balloons()\n",
    "                    elif t['pos'] > t['neg']:\n",
    "                        st.markdown(\"Speech Text is Classified as **Positive**. :smiley:\")\n",
    "                        st.balloons()\n",
    "                    elif t['neg'] > t['pos']:\n",
    "                        st.markdown(\"Speech Text is Classified as **Negative**. :disappointed:\")\n",
    "\n",
    "\n",
    "    st.sidebar.markdown(\n",
    "            \"\"\"\n",
    "     ----------\n",
    "    ## Project Overview\n",
    "    This is an AI web app that transcribes text to speech in 6 different languages and speech to text, to extract specific features in the \n",
    "    speech like NER, Sentiment and the Audio signal time frame.\n",
    "        \n",
    "    \"\"\")\n",
    "\n",
    "    st.sidebar.header(\"\")  # initialize empty space\n",
    "\n",
    "    st.sidebar.markdown(\n",
    "    \"\"\"\n",
    "    ----------\n",
    "    ## Instructions\n",
    "    1. For your text2Speech, select your input text language and your output speech language. Then use the convert button. \n",
    "    2. Upload your wav audio file to begin your speech2text analyses\n",
    "    3. If your file does not upload, make sure it's a mono-channel file, i.e having only one audio source.\n",
    "    4. If your file isn't wav formated, do not worry, [click here](https://www.movavi.com/support/how-to/how-to-convert-music-to-wav.html) \n",
    "       and head over to \"Online Converter\" to upload your mp3 file.\n",
    "    5. if you are getting \"Sorry...Run Again\" message when you upload file, try running again when you have a stable\n",
    "       network \n",
    "    \n",
    "    \"\"\")\n",
    "\n",
    "     # preview app demo\n",
    "    demo = st.sidebar.checkbox('App Demo')\n",
    "    if demo == 1:\n",
    "       st.sidebar.video('https://res.cloudinary.com/victorogunjobi/video/upload/v1657021658/Text-Speech-Analytic%20app/app_demo_rq2dpx.mp4', format='mp4')\n",
    "\n",
    "    st.sidebar.header(\"\")\n",
    "\n",
    "    st.sidebar.markdown(\n",
    "    \"\"\"\n",
    "    -----------\n",
    "    # Let's connect\n",
    "    \n",
    "    [![Victor Ogunjobi](https://img.shields.io/badge/Author-@VictorOgunjobi-gray.svg?colorA=gray&colorB=dodgergreen&logo=github)](https://www.github.com/chemicopy)\n",
    "    \n",
    "    [![Victor Ogunjobi](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logoColor=white)](https://www.linkedin.com/in/victor-ogunjobi-a761561a5/)\n",
    "    \n",
    "    [![Victor Ogunjobi](https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=gray)](https://twitter.com/chemicopy_)\n",
    "    \"\"\")\n",
    "\n",
    "     #----- deleting files from directories so we don't overload the app------\n",
    "    def remove_wav_files(n):\n",
    "        wav_files = glob.glob(\"temp2/*wav\")\n",
    "        if len(wav_files) != 0:\n",
    "            now = time.time()\n",
    "            n_days = n * 86400\n",
    "            for f in wav_files:\n",
    "                if os.stat(f).st_mtime < now - n_days:\n",
    "                   os.remove(f)\n",
    "                   print(\"Deleted\", f)\n",
    "\n",
    "    def remove_mp3_files(n):\n",
    "        mp3_files = glob.glob(\"temp/*mp3\")\n",
    "        if len(mp3_files) != 0:\n",
    "            now = time.time()\n",
    "            n_days = n * 86400\n",
    "            for f in mp3_files:\n",
    "                if os.stat(f).st_mtime < now - n_days:\n",
    "                   os.remove(f)\n",
    "                   print(\"Deleted\", f)\n",
    "\n",
    "\n",
    "    remove_mp3_files(7)   # remove mp3 files from directory\n",
    "\n",
    "    remove_wav_files(7)   # remove wav files from directory\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  }
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